The subject matter discussed in the background section should not be assumed to be prior art merely as a result of its mention in the background section. Similarly, a problem mentioned in the background section or associated with the subject matter of the background section should not be assumed to have been previously recognized in the prior art. The subject matter in the background section merely represents different approaches, which in and of themselves may also correspond to claimed embodiments.
Client organizations with datasets in their databases may benefit from predictive analysis. Unfortunately, there is no low cost and scalable solution in the marketplace today. Instead, client organizations must hire technical experts to develop customized mathematical constructs and predictive models which are very expensive. Consequently, client organizations without vast financial means are simply priced out of the market and thus do not have access to predictive analysis capabilities for their datasets.
Client organizations that have the financial means to hire technical and mathematical experts to develop the necessary mathematical constructs and predictive models suffer from a common problem with customized solutions. Specifically, the customized solution is tailored to the particular problem at hand at a given point in time, and as such, the customized solution is not able to accommodate changes to the underlying data structure, the customized solution is not able to accommodate changes to the types of data stored within the client's datasets, nor is the customized solution able to scale up to meet increasing and changing demands of the client as their business and dataset grows over time.
The present state of the art may therefore benefit from systems and methods for predictive query implementation and usage in an on-demand and/or multi-tenant database system as described herein.